Boilson, Andrew, Stephanie Gauttier, Regina Connolly, Paul Davis, Justin Connolly, Dale Weston, & Anthony Staines (2019). Q-method evaluation of a European health data analytic end user framework (September 12, 2019). 2019 ENTRENOVA Conference Proceedings, 12-14 September 2019, pp. 219-231. Rovinj, Croatia. SSRN Electronic Journal [Social Science Research Network]. (doi: 10.2139/ssrn.3490516) (Link: http://dx.doi.org/10.2139/ssrn.3490516) (Access: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3490516) (Conference Version: https://www.econstor.eu/bitstream/10419/207682/1/26-ENT-2019-Boilson-et-al-219-231.pdf)
Abstract: MIDAS (Meaningful Integration of Data Analytics and Services) project is developing a big data platform to facilitate the utilisation of a wide range of health and social care data to support better policy making. Our aim is to explore the use of Q-methodology as part of the evaluation of the implementation of the MIDAS project. Q-methodology is used to identify perspectives and viewpoints on a particular topic. In our case, we defined a concourse of statements relevant to project implementation and goals, by working from a logic model previously developed for the evaluation, and structured interviews with project participants. A 36-item concourse was delivered to participants, using the HTMLQ system. Analysis was done in the q-method package. Participants had a range of professional backgrounds, and a range of roles in the project, including developers, end-users, policy staff, and health professionals. The q-sort is carried out at 14 months into the project, a few months before the intended first release of the software being developed. Sixteen people took part, 6 developers, 5 managers, 2 health professionals and 3 others. Three factors (distinct perspectives) were identified in the data. These were tentatively labelled ‘Technical optimism’, ‘End-user focus’ and ‘End-user optimism’. These loaded well onto individuals, and there were few consensus statements. Analysis of these factors loaded well onto individuals with a significant number of consensus statements identified
Andrew Boilson <firstname.lastname@example.org> is a research fellow at Dublin City University, Dublin, Ireland.